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Updated: Feb 16, 2026

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
Published on: May 18, 2020
Normalized breast cancer survival outcomes in U.S. tumor registries
Tori C Nierenberg1, Kerri-Anne Crowell2, Samantha M Thomas3
1Department of Surgery, Duke University Medical Center, Durham, NC, USA.
Background:
This study normalized the National Cancer Database (NCDB) and Surveillance, Epidemiology, End Results Program (SEER) populations to mirror the USCS population and examined survival outcomes in breast cancer following normalization.
Methods:
Patients diagnosed with stage I-IV breast cancer (2010-2018) were selected from the NCDB and SEER. Rates obtained from the USCS were used to normalize the NCDB and SEER cohorts, using patient weighted frequencies for variables (age, sex, race/ethnicity, etc). Overall survival was estimated using the Kaplan-Meier method before and after normalization.
Results:
The USCS included 2473,739 patients, the NCDB 1441,556, and SEER 504,938. There were minimal differences between the cohorts based on age or sex. There were notable differences in the racial/ethnic composition (Hispanic: USCS 8.3 %, NCDB 5.9 %, SEER 11.7 %; p < 0.001). There were minimal differences in tumor biomarkers, but significant differences in extent of disease (local: USCS 66.1 %, NCDB 80.2 %, SEER 68.4 %; distant: USCS 6 %, NCDB 3.9 %, SEER 3.9 %; p < 0.001). Variables that were similar without weighting (age, sex, tumor biomarkers), had similar OS after weighting. However, when the NCDB and SEER were weighted by stage, HR status and race/ethnicity combined, slight changes were seen in 5-year OS (NCDB regional: unweighted 76.8 % vs weighted 77.0 %, SEER regional: unweighted 79.7 % vs weighted 78.5 %; p < 0.001).
Conclusions:
US tumor registries provide data for a large sampling of breast cancer patients. Despite significant differences in case coverage based on race/ethnicity and stage, OS remained similar following normalization to the USCS, suggesting that analyses using these data sets may be generalizable to the population.
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